Model fine tuning
We don’t just deploy pre-trained AI models — we fine-tune them to fit your specific business logic, domain vocabulary, and workflow nuances. Whether it's adapting open-source models like LLaMA 2-3 or DeepSeek to your internal knowledge base, or optimizing lightweight models for cost-efficient inference, we make sure the output isn't just smart — it's relevant. From structured prompt engineering to full adapter-based tuning, we align the model behavior with your real-world needs.
Agent implementation
We implement AI agents so they are fully integrated into your environment. Whether using n8n or writing completely custom business logic in Python, PHP, or Node.js — we tailor the implementation to your system, not the other way around. And we don’t stop at stateless replies — every agent we build comes with persistent memory using databases like Redis, Supabase, or SQL. That means it remembers, adapts, and stays useful over time — just like a real assistant should.
Agentic RAG (Retreival Augmented Generation)
We don’t fine-tune models just to have them go stale next month. Instead of locking knowledge into static weights, we implement Agentic RAG — where the agent dynamically retrieves the latest data, thinks through the task, and acts accordingly. New documents? Updated policies? No room for hallucinations? No problem. The agent knows how to fetch what it needs when it needs it. You stay current without retraining every time your content changes. That’s how real-time AI should work.
Private hosting
We handle privately hosted agents, tailored to your setup — not tied to someone else’s cloud. The agent runs where you need it, with the performance and privacy you expect, without burning your budget. No hidden platform fees. No lock-in. Just a system that works — fast, lean, and under your control.